code: def forward(self, x):
      # x: [batch_size, node_num, seq_len, input_dim]
      encode = self.first_encoder(x)
      encode = self.second_encoder(encode)
      out = self.out_fc(encode[:, -1, :]).squeeze().to(self.device)
  
      return out
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import pandas
  from typing import Text, Union
  import copy
  from ...utils import get_or_create_path
  from ...log import get_module_logger
  import torch
  import torch.nn
  import torch.optim
  from ...model.base import Model
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
  from .pytorch_krnn import CNNKRNNEncoder
label_desc: Use image node_id 2 for calling the SandwichModel obj's underlying member method code with example usage: obj.forward(x) and returns: out